Artificial Intelligence Models for the Mass Loss of Copper-Based Alloys under Cavitation
Cavitation is a physical process that produces different negative effects on the components working in conditions where it acts. One is the materials’ mass loss by corrosion–erosion when it is introduced into fluids under cavitation. This research aims at modeling the mass variation of three samples...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Materials |
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| Online Access: | https://www.mdpi.com/1996-1944/15/19/6695 |
| _version_ | 1797478440541093888 |
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| author | Cristian Ștefan Dumitriu Alina Bărbulescu |
| author_facet | Cristian Ștefan Dumitriu Alina Bărbulescu |
| author_sort | Cristian Ștefan Dumitriu |
| collection | DOAJ |
| description | Cavitation is a physical process that produces different negative effects on the components working in conditions where it acts. One is the materials’ mass loss by corrosion–erosion when it is introduced into fluids under cavitation. This research aims at modeling the mass variation of three samples (copper, brass, and bronze) in a cavitation field produced by ultrasound in water, using four artificial intelligence methods—SVR, GRNN, GEP, and RBF networks. Utilizing six goodness-of-fit indicators (R<sup>2</sup>, MAE, RMSE, MAPE, CV, correlation between the recorded and computed values), it is shown that the best results are provided by GRNN, followed by SVR. The novelty of the approach resides in the experimental data collection and analysis. |
| first_indexed | 2024-03-09T21:31:58Z |
| format | Article |
| id | doaj.art-f46af55111d941e0b9c515487d09077f |
| institution | Directory Open Access Journal |
| issn | 1996-1944 |
| language | English |
| last_indexed | 2024-03-09T21:31:58Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Materials |
| spelling | doaj.art-f46af55111d941e0b9c515487d09077f2023-11-23T20:55:14ZengMDPI AGMaterials1996-19442022-09-011519669510.3390/ma15196695Artificial Intelligence Models for the Mass Loss of Copper-Based Alloys under CavitationCristian Ștefan Dumitriu0Alina Bărbulescu1Doctoral School, Technical University of Civil Engineering Bucharest, 124, Lacul Tei Bd., 020396 Bucharest, RomaniaDepartment of Civil Engineering, Transilvania University of Brașov, 5, Turnului Street, 900152 Brașov, RomaniaCavitation is a physical process that produces different negative effects on the components working in conditions where it acts. One is the materials’ mass loss by corrosion–erosion when it is introduced into fluids under cavitation. This research aims at modeling the mass variation of three samples (copper, brass, and bronze) in a cavitation field produced by ultrasound in water, using four artificial intelligence methods—SVR, GRNN, GEP, and RBF networks. Utilizing six goodness-of-fit indicators (R<sup>2</sup>, MAE, RMSE, MAPE, CV, correlation between the recorded and computed values), it is shown that the best results are provided by GRNN, followed by SVR. The novelty of the approach resides in the experimental data collection and analysis.https://www.mdpi.com/1996-1944/15/19/6695mass losscavitationultrasoundcorrosion–erosionartificial intelligence (AI) |
| spellingShingle | Cristian Ștefan Dumitriu Alina Bărbulescu Artificial Intelligence Models for the Mass Loss of Copper-Based Alloys under Cavitation Materials mass loss cavitation ultrasound corrosion–erosion artificial intelligence (AI) |
| title | Artificial Intelligence Models for the Mass Loss of Copper-Based Alloys under Cavitation |
| title_full | Artificial Intelligence Models for the Mass Loss of Copper-Based Alloys under Cavitation |
| title_fullStr | Artificial Intelligence Models for the Mass Loss of Copper-Based Alloys under Cavitation |
| title_full_unstemmed | Artificial Intelligence Models for the Mass Loss of Copper-Based Alloys under Cavitation |
| title_short | Artificial Intelligence Models for the Mass Loss of Copper-Based Alloys under Cavitation |
| title_sort | artificial intelligence models for the mass loss of copper based alloys under cavitation |
| topic | mass loss cavitation ultrasound corrosion–erosion artificial intelligence (AI) |
| url | https://www.mdpi.com/1996-1944/15/19/6695 |
| work_keys_str_mv | AT cristianstefandumitriu artificialintelligencemodelsforthemasslossofcopperbasedalloysundercavitation AT alinabarbulescu artificialintelligencemodelsforthemasslossofcopperbasedalloysundercavitation |